{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from imitation.algorithms.dagger import _load_trajectory\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "traj = _load_trajectory(\"/home/andrea/policy_compression/results/sampling_overlap_2021_04_16/tmp_sampling_overlap/0/demos/round-000/dagger-demo-20210415_191909_3c36af.npz\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Copy-pasted from quad evaluation\n",
    "def plot_trajectories(axs, obs_hist, act_hist):\n",
    "    # Constraints\n",
    "    #p_min = -3.0\n",
    "    #p_max = 3.0\n",
    "    #v_min = -1.5\n",
    "    #v_max = 1.5\n",
    "    T_max = len(obs_hist)-1\n",
    "    axis_name = [\"x\", \"y\", \"z\"]\n",
    "    for ax in range(3):\n",
    "        # position\n",
    "        axs[0, ax].plot([5.0*state[ax] for state in obs_hist])\n",
    "        #axs[0, ax].hlines(y=[p_min, p_max], xmin=0, xmax=T_max, linestyles='dashed', color='red')\n",
    "        axs[0, ax].set_ylabel(f'Pos-{axis_name[ax]}(m)')\n",
    "            \n",
    "        # velocity\n",
    "        axs[1, ax].plot([state[ax+3] for state in obs_hist])\n",
    "        #axs[1, ax].hlines(y=[v_min, v_max], xmin=0, xmax=T_max, linestyles='dashed', color='red')\n",
    "        axs[1, ax].set_ylabel(f'Vel-{axis_name[ax]} (m/s)')\n",
    "\n",
    "    # Roll, pitch, thrust \n",
    "    axs[2, 0].plot([5.0*state[6]/np.pi*180 for state in obs_hist], label='roll')\n",
    "    axs[2, 0].plot([12.0*cmd[0]/np.pi*180 for cmd in act_hist], label='roll_cmd')\n",
    "    axs[2, 0].set_ylabel(\"Attitude Roll (Rad)\")\n",
    "    axs[2, 0].set_xlabel('Time (steps)')\n",
    "    #axs[2, 0].legend()\n",
    "    \n",
    "    axs[2, 1].plot([5.0*state[7]/np.pi*180 for state in obs_hist], label='pitch')\n",
    "    axs[2, 1].plot([12.0*cmd[1]/np.pi*180 for cmd in act_hist], label='pitch_cmd')\n",
    "    axs[2, 1].set_ylabel(\"Attitude Pitch (Rad)\")\n",
    "    axs[2, 1].set_xlabel('Time (steps)')\n",
    "    #axs[2, 1].legend()\n",
    "\n",
    "    axs[2, 2].plot([12.0*cmd[2] for cmd in act_hist], label='cmd_thrust')\n",
    "    axs[2, 2].set_ylabel('Thrust (N)')\n",
    "    axs[2, 2].set_xlabel('Time (steps)')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<Figure size 432x288 with 9 Axes>"
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     },
     "metadata": {
      "needs_background": "light"
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   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "fig_t, axs_t = plt.subplots(3, 3, sharex=True, sharey=\"row\")\n",
    "plot_trajectories(axs_t, list(traj.obs), list(traj.acts))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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